REACT: Context-Sensitive Recommendations for Data Analysis
Summary: REACT: context-sensitive recommendations for analysis actions, integrated into the UI. Holistic similarity across actions, data, and workflow maps current sessions to prior analysts to suggest next steps; demonstrated on honeypot data for cyber-forensics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tova Milo
- 2. Amit Somech
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,540 | Automating Exploratory Data Analysis via Machine Learning: An Overview | 2020 | SIGMOD | 6.1033443e-05 |
| 8,296 | Modern Recommender Systems: from Computing Matrices to Thinking with Neurons | 2018 | SIGMOD | 4.5435639e-05 |
| 9,830 | Towards Autonomous, Hands-Free Data Exploration | 2020 | CIDR | 4.2751057e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 460 | SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics | 2015 | VLDB | 0.00022516069 |
| 1,009 | SnipSuggest: Context-Aware Autocompletion for SQL | 2011 | VLDB | 0.00014653644 |
| 4,996 | RecDB in Action: Recommendation Made Easy in Relational Databases | 2013 | VLDB | 5.7786825e-05 |
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